Biology subjects

Fahey, P. G.

Publications and source records attributed to Fahey, P. G..

17 recordsLinked to original sources

bioRxiv2023

Towards a Foundation Model of the Mouse Visual Cortex

The complexity of neural circuits makes it challenging to decipher the brains algorithms of intelligence. Recent break-throughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brains computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility. The emergence of foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. It could also be adapted to new tasks beyond neural prediction, accurately predicting anatomical cell types, dendritic features, and neuronal connectivity within the MICrONS functional connectomics dataset. Our work is a crucial step toward building foundation brain models. As neuroscience accumulates larger, multi-modal datasets, foundation models will uncover statistical regularities, enabling rapid adaptation to new tasks and accelerating research.

neuroscience↗

bioRxiv2023

Bipartite invariance in mouse primary visual cortex

A primary goal of sensory systems is to extract robust and meaningful features that are invariant to variations in the sensory input. Characterizing these invariances at the neuronal level is crucial for understanding how the visual system supports generalization, but the high-dimensional nature of ecological stimuli poses major challenges. Consequently, our understanding of how the brain represents invariances has historically depended on a few examples, such as phase invariance to grating stimuli in V1 complex cells. Here, we leverage the inception loop paradigm --iterating between large-scale recordings, deep learning neuronal predictive models, and in silico experiments with in vivo verification--to characterize neuronal invariances in mouse V1. Using a neuronal predictive model, we synthesized Diverse Exciting Inputs (DEIs) that strongly drive target neurons while differing substantially in image space. These DEIs revealed a novel bipartite invariance: one portion of the receptive field encodes shift-invariant, high-frequency textures, while the other encodes a fixed, low-frequency spatial pattern. This subfield division aligned with object boundaries defined by spatial frequency differences in highly activating stimuli, suggesting bi-partite invariance contributes to segmentation. Our analysis of computational models and anatomical data from the MICrONS dataset revealed a hierarchical organization of excitatory neurons in mouse V1 Layers 2/3: We found that postsynaptic neurons exhibited greater invariance than their presynaptic inputs, while neurons with lower invariance formed more connections. These findings suggest a synaptic-level hierarchy that progressively increases neural invariance within the primary visual cortex. Intriguingly, similar high-low frequency bipartite patterns strongly activate certain units in artificial neural networks, suggesting that universal visual representations govern both biological and artificial systems, potentially aiding in the extraction of visual features from complex backgrounds.

neuroscience↗

bioRxiv2023

NEURD: A mesh decomposition framework forautomated proofreading and morphologicalanalysis of neuronal EM reconstructions

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

neuroscience↗

bioRxiv2023

Functional connectomics reveals general wiring rule in mouse visual cortex

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain implements computation. In the mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited to within V1, leaving much unknown about broader connectivity rules. In this study, we leverage the millimeter-scale MICrONS dataset to analyze synaptic connectivity and functional properties of individual neurons across cortical layers and areas. Our results reveal that neurons with similar responses are preferentially connected both within and across layers and areas -- including feedback connections -- suggesting the universality of the like-to-like connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections, beyond what could be explained by the physical proximity of axons and dendrites. We also found a higher-order rule where postsynaptic neuron cohorts downstream of individual presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Notably, recurrent neural networks (RNNs) trained on a simple classification task develop connectivity patterns mirroring both pairwise and higher-order rules, with magnitude similar to those in the MICrONS data. Lesion studies in these RNNs reveal that disrupting like-to-like connections has a significantly greater impact on performance compared to lesions of random connections. These findings suggest that these connectivity principles may play a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

neuroscience↗

bioRxiv2023

Pattern completion and disruption characterize contextual modulation in mouse visual cortex

Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also by surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent stimuli--where the center and surround share the same orientation--are more inhibitory than when orientations are orthogonal, potentially serving redundancy reduction and predictive coding. Understanding these center-surround interactions in relation to natural image statistics is challenging due to the high dimensionality of the stimulus space, yet crucial for deciphering the neuronal code of real-world sensory processing. Utilizing large-scale recordings from mouse V1, we trained convolutional neural networks (CNNs) to predict and synthesize surround patterns that either optimally suppressed or enhanced responses to center stimuli, confirmed by in v ivo experiments. Contrary to the notion that congruent stimuli are suppressive, we found that surrounds that completed patterns based on natural image statistics were facilitatory, while disruptive surrounds were suppressive. Applying our CNN image synthesis method in macaque V1, we discovered that pattern completion within the near surround occurred more frequently with excitatory than with inhibitory surrounds, suggesting that our results in mice are conserved in macaques. Further, experiments and model analyses confirmed previous studies reporting the opposite effect with grating stimuli in both species. Using the MICrONS functional connectomics dataset, we observed that neurons with similar feature selectivity formed excitatory connections regardless of their receptive field overlap, aligning with the pattern completion phenomenon observed for excitatory surrounds. Finally, our empirical results emerged in a normative model of perception implementing Bayesian inference, where neuronal responses are modulated by prior knowledge of natural scene statistics. In summary, our findings identify a novel relationship between contextual information and natural scene statistics and provide evidence for a role of contextual modulation in hierarchical inference.

neuroscience↗

bioRxiv2023

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

neuroscience↗

bioRxiv2022

Large-scale unsupervised discovery of excitatory morphological cell types in mouse visual cortex

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological "bar code" describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2-3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5 which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons morphological diversity is better understood by considering axes of variation than using distinct m-types.

neuroscience↗

bioRxiv2022

It takes neurons to understand neurons: Digital twins of visual cortex synthesize neural metamers

Metamers, images that are perceived as equal, are a useful tool to study representations of natural images in biological and artificial vision systems. We synthesized metamers for the mouse visual system by inverting a deep encoding model to find an image that matched the observed neural activity to the original presented image. When testing the resulting images in physiological experiments we found that they most closely reproduced the neural activity of the original image when compared to other decoding methods, even when tested in a different animal whose neural activity was not used to produce the metamer. This demonstrates that deep encoding models do capture general characteristic properties of biological visual systems and can be used to define a meaningful perceptual loss for the visual system.

neuroscience↗

bioRxiv2022

Quantitative Census of Local Somatic Features in Mouse Visual Cortex

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. Cellular scale electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. It also presents a clear challenge for analysis to identify cell-types in data that contains tens of thousands of neurons, most of which have incomplete reconstructions. To address this challenge, we present the first systematic survey of the somatic region of all cells within a cubic millimeter of cortex using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to identify cell-types, including types defined primarily based on their connectivity patterns. We then describe how this classification facilitates cell type specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.

neuroscience↗

bioRxiv2021

Petascale neural circuit reconstruction: automated methods

3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach has been limited to small chunks of mammalian brains. To scale up to larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, a popular form of 3D EM. The pipeline employs convolutional nets to compute the nonsmooth transformations required to align images of serial sections containing numerous cracks and folds, detect neuronal boundaries, label voxels as axon, dendrite, soma, and other semantic categories, and detect synapses and assign them to presynaptic and postsynaptic segments. The output of neuronal boundary detection is segmented by mean affinity agglomeration with semantic and size constraints. Pipeline operations are implemented by leveraging distributed and cloud computing. Intermediate results of the pipeline are held in cloud storage, and can be effortlessly viewed as images, which aids debugging. We applied the pipeline to create an automated reconstruction of an EM image volume spanning four visual cortical areas of a mouse brain. Code for the pipeline is publicly available, as is the reconstructed volume.

neuroscience↗

bioRxiv2021

A CRISPR toolbox for generating intersectional genetic mice for functional, molecular, and anatomical circuit mapping

BackgroundA full understanding of circuits and cellular mechanisms governing health and disease requires the dissection and multi-faceted study of discrete cell subtypes in developing and adult animal models. Recombinase-driven expression of transgenic response alleles represents a significant and powerful approach to delineate cell populations for functional, molecular, and anatomical study. In addition to single recombinase systems, the expression of two recombinases in distinct, but partially overlapping, populations allow for more defined target expression. Although the application of this method is becoming increasingly popular, the expense and difficulty associated with production of customized intersectional mouse lines have limited widespread application to more common allele manipulations that are often commercially produced at great expense. ResultsWe present a simplified CRISPR toolkit for rapid, inexpensive, and facile intersectional allele production. Briefly, we produced 7 intersectional mouse lines using a dual recombinase system, one mouse line with a single recombinase system, and three embryonic stem (ES) cell lines that are designed to study how functional, molecular, and anatomical features relate to each other in building circuits that underlie physiology and behavior. As a proof-of-principle, we applied three of these lines to different neuronal populations for anatomical mapping and functional in vivo investigation of respiratory control. We also generated a mouse line with a single recombinase-responsive allele that controls the expression of the calcium sensor Twitch-2B. This mouse line was applied globally to study the effects of follicle stimulating hormone (FSH) and luteinizing hormone (LH) on calcium release in the ovarian follicle. ConclusionsLines presented here are representative examples of outcomes possible with the successful application of our genetic toolkit for the facile development of diverse, modifiable animal models. This toolkit will allow labs to create single or dual recombinase effector lines easily for any cell population or subpopulation of interest when paired with the appropriate Cre and FLP recombinase mouse lines or viral vectors. We have made our tools and derivative intersectional mouse and ES cell lines openly available for non-commercial use through publicly curated repositories for plasmid DNA, ES cells, and transgenic mouse lines.

genetics↗

bioRxiv2019

A global map of orientation tuning in mouse visual cortex

In primates and most carnivores, neurons in primary visual cortex are spatially organized by their functional properties. For example, neurons with similar orientation preferences are grouped together in iso-orientation domains that smoothly vary over the cortical sheet. In rodents, on the other hand, neurons with different orientation preferences are thought to be spatially intermingled, a feature which has been termed \"salt-and-pepper\" organization. The apparent absence of any systematic structure in orientation tuning has been considered a defining feature of the rodent visual system for more than a decade, with broad implications for brain development, visual processing, and comparative neurophysiology. Here, we revisited this question using new techniques for wide-field two-photon calcium imaging that enabled us to collect nearly complete population tuning preferences in layers 2-4 across a large fraction of the mouse visual hierarchy. Examining the orientation tuning of these hundreds of thousands of neurons, we found a global map spanning multiple visual cortical areas in which orientation bias was organized around a single pinwheel centered in V1. This pattern was consistent across animals and cortical depth. The existence of this global organization in rodents has implications for our understanding of visual processing and the principles governing the ontogeny and phylogeny of the visual cortex of mammals.

neuroscience↗

bioRxiv2019

Cell type composition and circuit organization of neocortical radial clones

SummaryExcitatory neurons arising from a common progenitor establish radially-oriented clonal units in the neocortex which have been proposed to serve as elementary information processing modules. To characterize the cell types and circuit diagram within these clonal units, we performed single-cell RNA-sequencing and multi-cell patch clamp recordings of neurons derived from Nestin-positive progenitors. We found that radial clones do not appear to be fate-restricted, but instead individual clones are composed of a random sampling of the transcriptomic cell types present in a particular cortical area. The effect of lineage on synaptic connectivity depends on the type of connection tested: pairs of clonally related neurons were more likely to be connected vertically, across cortical layers, but not laterally within the same layer, compared to unrelated pairs. We propose that integration of vertical input from related neurons with lateral input from unrelated neurons may represent a developmentally programmed motif for assembling neocortical circuits.

neuroscience↗

bioRxiv2019

Follicle-stimulating hormone and luteinizing hormone increase Ca2+ in the granulosa cells of mouse ovarian follicles

Summary sentenceBoth FSH and LH increase Ca2+ in the granulosa cells of intact ovarian follicles from mice expressing genetically encoded sensors.\n\nABSTRACTIn mammalian ovarian follicles, follicle stimulating hormone (FSH) and luteinizing hormone (LH) signal primarily through the G-protein Gs to elevate cAMP, but both of these hormones can also elevate Ca2+ under some conditions. Here we investigate FSH- and LH-induced Ca2+ signaling in intact follicles of mice expressing genetically encoded Ca2+ sensors, Twitch-2B and GCaMP6s. At a physiological concentration (1 nM), FSH elevates Ca2+ within the granulosa cells of preantral and antral follicles. The Ca2+ rise begins several minutes after FSH application, peaks at ~10 minutes, remains above baseline for another ~10 minutes, and depends on extracellular Ca2+. However, suppression of the FSH-induced Ca2+ increase by reducing extracellular Ca2+ does not inhibit FSH-induced phosphorylation of MAP kinase, estradiol production, or the acquisition of LH responsiveness. Like FSH, LH also increases Ca2+, when applied to preovulatory follicles. At a physiological concentration (10 nM), LH elicits Ca2+ oscillations in a subset of cells in the outer mural granulosa layer. These oscillations continue for at least 6 hours and depend on the activity of Gq family G-proteins. Suppression of the oscillations by Gq inhibition does not inhibit meiotic resumption, but does slightly attenuate ovulation. In summary, both FSH and LH increase Ca2+ in the granulosa cells of intact follicles, but the functions of these Ca2+ rises are only starting to be identified.

cell biology↗

bioRxiv2018

Inception in visual cortex: in vivo-silico loops reveal most exciting images

Much of our knowledge about sensory processing in the brain is based on quasi-linear models and the stimuli that optimally drive them. However, sensory information processing is nonlinear, even in primary sensory areas, and optimizing sensory input is difficult due to the high-dimensional input space. We developed inception loops, a closed-loop experimental paradigm that combines in vivo recordings with in silico nonlinear response modeling to identify the Most Exciting Images (MEIs) for neurons in mouse V1. When presented back to the brain, MEIs indeed drove their target cells significantly better than the best stimuli identified by linear models. The MEIs exhibited complex spatial features that deviated from the textbook ideal of V1 as a bank of Gabor filters. Inception loops represent a widely applicable new approach to dissect the neural mechanisms of sensation.

neuroscience↗

bioRxiv2018

Stimulus domain transfer in recurrent models for large scale cortical population prediction on video

To better understand the representations in visual cortex, we need to generate better predictions of neural activity in awake animals presented with their ecological input: natural video. Despite recent advances in models for static images, models for predicting responses to natural video are scarce and standard linear-nonlinear models perform poorly. We developed a new deep recurrent network architecture that predicts inferred spiking activity of thousands of mouse V1 neurons simulta-neously recorded with two-photon microscopy, while accounting for confounding factors such as the animals gaze position and brain state changes related to running state and pupil dilation. Powerful system identification models provide an opportunity to gain insight into cortical functions through in silico experiments that can subsequently be tested in the brain. However, in many cases this approach requires that the model is able to generalize to stimulus statistics that it was not trained on, such as band-limited noise and other parameterized stimuli. We investigated these domain transfer properties in our model and find that our model trained on natural images is able to correctly predict the orientation tuning of neurons in responses to artificial noise stimuli. Finally, we show that we can fully generalize from movies to noise and maintain high predictive performance on both stimulus domains by fine-tuning only the final layers weights on a network otherwise trained on natural movies. The converse, however, is not true.

neuroscience↗

bioRxiv2017

Increased axonal bouton stability during learning in the mouse model of MECP2 duplication syndrome

MECP2-duplication syndrome is an X-linked form of syndromic autism caused by genomic duplication of the region encoding Methyl-CpG-binding protein 2. Mice overexpressing MECP2 demonstrate altered patterns of learning and memory, including enhanced motor learning. Previous work associated this enhanced motor learning to abnormally increased stability of dendritic spine clusters formed in the apical tuft of corticospinal, area M1, neurons during rotarod training. In the current study, we measure the structural plasticity of axonal boutons in Layer 5 (L5) pyramidal neuron projections to layer 1 of area M1 during motor learning. In wild-type mice we find that during rotarod training, bouton formation rate changes minimally, if at all, while bouton elimination rate doubles. Notably, the observed upregulation in bouton elimination with learning is absent in MECP2-duplication mice. This result provides further evidence of imbalance between structural stability and plasticity in this form of syndromic autism. Furthermore, the observation that axonal bouton elimination doubles with motor learning in wild-type animals contrasts with the increase of dendritic spine consolidation observed in corticospinal neurons at the same layer. This dissociation suggests that different area M1 microcircuits may manifest different patterns of structural synaptic plasticity during motor learning.\n\nSIGNIFICANCE STATEMENTAbnormal balance between synaptic stability and plasticity is a feature of several autism spectrum disorders, often corroborated by in vivo studies of dendritic spine turnover. Here we provide the first evidence that abnormally increased stability of axonal boutons, the presynaptic component of excitatory synapses, occurs during motor learning in the MECP2 duplication syndrome mouse model of autism. In contrast, in normal controls, axonal bouton elimination in L5 pyramidal neuron projections to layer 1 of area M1 doubles with motor learning. The fact that axonal projection boutons get eliminated, while corticospinal dendritic spines get consolidated with motor learning in layer 1 of area M1, suggests that structural plasticity manifestations differ across different M1 microcircuits.

neuroscience↗